EMNLP is one of the most highly regarded Natural Language Programming (NLP) conferences in the world and is organized by the Association for Computational Linguistics. The researchers' paper was selected from more than 2,100 papers submitted to this year's conference, to be held in Brussels, Belgium, Nov. 2-4.

"LISA brings together two relatively disjointed schools of thought regarding machine learning and language analysis by combining deep learning and linguistic formalisms. This allows the model to more effectively utilize syntactic parses to obtain semantic meaning," said Verga. "The task of syntactic parsing partitions a sentence into its grammatical structure, a tree consisting of nouns, verbs, their direct objects, and so forth. Semantic role labeling, however, is the process by which a computer is able to separate a written statement into sections based upon its overall meaning, i.e. identifying who did what to whom."

According to the researchers, they have engineered a method in which excerpts of writing can be separated into their respective syntactic pieces, while also labeling the writing with notations of its overall significance. "We developed a new technique for integrating syntax into a neural network model known as multi-head self-attention," said Strubell. "This innovation allowed our model to perform substantially better than any other model at the task of semantic role labeling."

Leveraging linguistic structure also allows the model to better generalize to writing styles across different domains. "This addresses a common problem in machine learning," said Verga. "Most comprehension models don't transfer well to understanding different forms of writing." Typically, Verga adds, a computer can only easily parse the writing styles it has been taught to analyze, but this new model can parse and assign semantic meaning to writing produced in different domains, such as journalism and fiction writing.

Strubell and Verga's research also combines the steps involved in syntactic parsing and semantic role labeling into a single maneuver. Prior to their integration of the numerous operations previously involved in the task, a computer gathered the necessary information by running through multiple models performing redundant computation. Now, the researchers' model obtains all of the desired information regarding the structure and meaning of a text into a single condensed action, requiring far fewer computational resources.